基于深度学习的鲕粒智能检测与特征统计
Oolitic intelligent detection and feature statistics based on deep learning
张晓燕 1李艳 2芦碧波 1侯广顺 3邢智峰 3杨晓芃4
作者信息
- 1. 河南理工大学计算机科学与技术学院,河南焦作 454003
- 2. 焦作冶金建材高级技工学校计算机科学与技术学院,河南焦作 454003
- 3. 河南理工大学资源与环境学院,河南焦作 454003
- 4. 河南理工大学测绘与国土信息工程学院,河南焦作 454003
- 折叠
摘要
鲕粒是一类特殊的沉积颗粒,其分布的疏密程度、粒径大小等信息可以直观地反映形成环境的水深以及水动力条件,具有重要的地质意义.在地质学中,通常将鲕粒岩石标本磨制成岩石薄片,并依靠专业人员在显微镜下观察来获取鲕粒含量、圆度、径粒大小等数据的估计值,存在着计算量大、成本高、周期长、人力投入大等缺点,而且该方法受主观因素影响较大,不同专家得出的结果也不尽相同.针对上述问题,本文提出了基于深度学习的鲕粒智能检测与特征统计方法,主要采用YOLOv5检测模型对鲕粒岩石薄片显微图像进行检测,并在YOLOv5网络主干部分添加轻量级的SE-Net通道注意力机制模块来提升检测性能;其次,本次使用DIoU-NMS替换NMS方法,改善图像中鲕粒分布拥挤时的漏检问题.实验证明改进后的算法最终精确率达到了98.8％,比原算法提升了1.3％.最后利用图像处理技术,对检测结果进行量化统计和分析,得到图像中鲕粒含量、圆度信息、粒径大小的统计结果直方图,为地质工作人员进行相关工作提供了极大的便利.
Abstract
Oolite is a special kind of sedimentary particles,and its distribution density,grain size and other information can intuitively reflect the water depth and hydrodynamic conditions of the formation environment.It has important geological significance.In geology,oolitic rock specimens are usually ground into rock thin sections which are then observed under the microscope by professionals for obtaining the estimated values of oolitic content,roundness,grain size and so on.This method has various shortcomings including the large amount of computation,high cost,long period and large manpower input,etc.Moreover,this method is greatly affected by subjective factors,with relatively different results obtained by different experts.To address the above problems,in this paper,we have proposed a deep learning-based oolite intelligent detection and feature statistics method.The YOLOv5 detection model is mainly adopted for detecting micrographs of oolite rock thin sections,and a lightweight SE-Net channel attention mechanism module is then added into the backbone part of the YOLOv5 network for improving the detection performance.Then,the NMS method is replaced by the DIoU-NMS method for improving the problem of missing detection when the oolite distribution is crowded in the image.The experimental results show that final accuracy of the improved algorithm reached 98.8％,which was 1.3％higher than that of the original algorithm.Finally,quantitative statistics and analysis of the detection results have been carried out by using the image processing technology,and histograms of the statistical results of the oolite content,roundness information and grain size in images have been obtained.The great convenience is provided to geological staffs for carrying out related works.
关键词
深度学习/改进YOLOv5/鲕粒检测/注意力机制/统计分析Key words
deep learning/improved YOLOv5/oolitic detection/attention mechanism/statistical analysis引用本文复制引用
基金项目
国家自然科学基金(41773024)
河南省高等学校重点科研项目(21A520016)
河南省本科高等学校省级大学生创新创业训练计划(S202110460005)
出版年
2024
NETL
NSTL
万方数据